{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:45:00Z","timestamp":1778600700064,"version":"3.51.4"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"11","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>The number of applications in sentiment analysis is growing daily, and research in this field is increasing. Despite the rapid growth of data sources in English, low-resource languages suffer from a lack of data for accurate training models. Moreover, users cannot trust such systems without explaining the output. In this study, we propose a cross-lingual deep neural model to improve the accuracy of sentiment analysis for low-resource languages while providing an explainable description of the predictions. The proposed model contains a word representation model where we use XLM-RoBERTa, a pre-trained contextualized transformer-based cross-lingual language model, and a long short-term memory network together with an attention mechanism that helps improve the explainability of the model and detect the informative words that impact text polarity. Our experiments show the superiority of the proposed model compared to the state-of-the-art mono-lingual techniques and cross-lingual models. The results show 0.55% improvement compared to the cross-lingual sentiment analysis proposed by Ghasemi et\u00a0al. and 15.08% improvement compared to the mono-lingual contextualized sentiment analysis. Moreover, we achieve 0.54% further improvement when using attention mechanisms for enhancing the model with explainability.<\/jats:p>","DOI":"10.1145\/3626094","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T12:15:25Z","timestamp":1696853725000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["How a Deep Contextualized Representation and Attention Mechanism Justifies Explainable Cross-Lingual Sentiment Analysis"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4477-6401","authenticated-orcid":false,"given":"Rouzbeh","family":"Ghasemi","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8110-1342","authenticated-orcid":false,"given":"Saeedeh","family":"Momtazi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Aspect and opinion term extraction for hotel reviews using transfer learning and auxiliary labels","author":"Winatmoko Yosef Ardhito","year":"2019","unstructured":"Yosef Ardhito Winatmoko, Ali Akbar Septiandri, and Arie Pratama Sutiono. 2019. Aspect and opinion term extraction for hotel reviews using transfer learning and auxiliary labels. arXiv e-prints, arXiv:1909.11879 (2019).","journal-title":"arXiv e-prints"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Artetxe Mikel","year":"2018","unstructured":"Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. Generalizing and improving bilingual word embedding mappings with a multi-step framework of linear transformations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"issue":"2","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"e7467","DOI":"10.1002\/cpe.7467","article-title":"Generative adversarial network for sentiment-based stock prediction","volume":"35","author":"Asgarian Sepehr","year":"2023","unstructured":"Sepehr Asgarian, Rouzbeh Ghasemi, and Saeedeh Momtazi. 2023. Generative adversarial network for sentiment-based stock prediction. Concurrency and Computation: Practice and Experience 35, 2 (2023), e7467.","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"e_1_3_2_6_2","first-page":"2855","volume-title":"Proceedings of the 12th Language Resources and Evaluation Conference","author":"Asli Seyed Arad Ashrafi","year":"2020","unstructured":"Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, and Omid Momenzadeh. 2020. Optimizing annotation effort using active learning strategies: A sentiment analysis case study in Persian. In Proceedings of the 12th Language Resources and Evaluation Conference. 2855\u20132861."},{"issue":"4","key":"e_1_3_2_7_2","first-page":"799","article-title":"The impact of Persian news on stock returns through text mining techniques","volume":"14","author":"Azizi Zahra","year":"2021","unstructured":"Zahra Azizi, Neda Abdolvand, Hassan Ghalibaf Asl, and Saeedeh Rajaee Harandi. 2021. The impact of Persian news on stock returns through text mining techniques. Iranian Journal of Management Studies 14, 4 (2021), 799\u2013816.","journal-title":"Iranian Journal of Management Studies"},{"key":"e_1_3_2_8_2","volume-title":"Proceedings of the 11th Conference and Labs of the Evaluation Forum (CLEF\u201920)","author":"Baruah Arup","year":"2020","unstructured":"Arup Baruah, K. Das, F. Barbhuiya, and Kuntal Dey. 2020. Automatic detection of fake news spreaders using BERT. In Proceedings of the 11th Conference and Labs of the Evaluation Forum (CLEF\u201920)."},{"key":"e_1_3_2_9_2","first-page":"100","article-title":"Explainability methods for natural language processing: Applications to sentiment analysis","volume":"2646","author":"Bodria Francesco","year":"2020","unstructured":"Francesco Bodria, Andr\u00e9 Panisson, Alan Perotti, and Simone Piaggesi. 2020. Explainability methods for natural language processing: Applications to sentiment analysis. CEUR Workshop Proceedings 2646 (2020), 100\u2013107.","journal-title":"CEUR Workshop Proceedings"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.31"},{"key":"e_1_3_2_11_2","first-page":"3829","volume-title":"Proceedings of the 13th Language Resources and Evaluation Conference","author":"Cambria Erik","year":"2022","unstructured":"Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, and Kenneth Kwok. 2022. SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In Proceedings of the 13th Language Resources and Evaluation Conference. 3829\u20133839."},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10462-022-10183-8","article-title":"State of the art: A review of sentiment analysis based on sequential transfer learning","author":"Chan Jireh Yi-Le","year":"2023","unstructured":"Jireh Yi-Le Chan, Khean Thye Bea, Steven Mun Hong Leow, Seuk Wai Phoong, and Wai Khuen Cheng. 2023. State of the art: A review of sentiment analysis based on sequential transfer learning. Artificial Intelligence Review 56 (2023), 749\u2013780.","journal-title":"Artificial Intelligence Review"},{"key":"e_1_3_2_13_2","volume-title":"Proceedings of the NeurIPS 2019 Workshop on Robust AI in Financial Services","author":"Chen Hanjie","year":"2019","unstructured":"Hanjie Chen and Yangfeng Ji. 2019. Improving the explainability of neural sentiment classifiers via data augmentation. In Proceedings of the NeurIPS 2019 Workshop on Robust AI in Financial Services."},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.5220\/0010215303190328","volume-title":"Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications\u2014Volume 1: WUDESHI-DR","author":"Cirqueira Douglas","year":"2020","unstructured":"Douglas Cirqueira, Fernando Almeida, G\u00fcltekin Cakir, Antonio Jacob, Fabio Lobato, Marija Bezbradica, and Markus Helfert.2020. Explainable sentiment analysis application for social media crisis management in retail. In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications\u2014Volume 1: WUDESHI-DR. 319\u2013328."},{"key":"e_1_3_2_15_2","article-title":"Unsupervised cross-lingual representation learning at scale","author":"Conneau Alexis","year":"2019","unstructured":"Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm\u00e1n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Unsupervised cross-lingual representation learning at scale. arXiv e-prints, arXiv:1911.02116 (2019).","journal-title":"arXiv e-prints"},{"key":"e_1_3_2_16_2","volume-title":"Advances in Neural Information Processing Systems","author":"Conneau Alexis","year":"2019","unstructured":"Alexis Conneau and Guillaume Lample. 2019. Cross-lingual language model pretraining. In Advances in Neural Information Processing Systems. Curran Associates, 7059\u20137069."},{"key":"e_1_3_2_17_2","first-page":"447","volume-title":"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing","author":"Danilevsky Marina","year":"2020","unstructured":"Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020. A survey of the state of explainable AI for natural language processing. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. 447\u2013459."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/978-3-030-00563-4_58","volume-title":"Proceedings of the International Conference on Brain Inspired Cognitive Systems","author":"Dashtipour Kia","year":"2018","unstructured":"Kia Dashtipour, Mandar Gogate, Ahsan Adeel, Cosimo Ieracitano, Hadi Larijani, and Amir Hussain. 2018. Exploiting deep learning for Persian sentiment analysis. In Proceedings of the International Conference on Brain Inspired Cognitive Systems. 597\u2013604."},{"issue":"5","key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"596","DOI":"10.3390\/e23050596","article-title":"Sentiment analysis of Persian movie reviews using deep learning","volume":"23","author":"Dashtipour Kia","year":"2021","unstructured":"Kia Dashtipour, Mandar Gogate, Ahsan Adeel, Hadi Larijani, and Amir Hussain. 2021. Sentiment analysis of Persian movie reviews using deep learning. Entropy 23, 5 (2021), 596.","journal-title":"Entropy"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.020"},{"issue":"1","key":"e_1_3_2_21_2","first-page":"1","article-title":"Extending Persian sentiment lexicon with idiomatic expressions for sentiment analysis","volume":"12","author":"Dashtipour Kia","year":"2022","unstructured":"Kia Dashtipour, Mandar Gogate, Alexander Gelbukh, and Amir Hussain. 2022. Extending Persian sentiment lexicon with idiomatic expressions for sentiment analysis. Social Network Analysis and Mining 12, 1 (2022), 1\u201313.","journal-title":"Social Network Analysis and Mining"},{"key":"e_1_3_2_22_2","first-page":"207","volume-title":"Progresses in Artificial Intelligence and Neural Systems","author":"Dashtipour Kia","year":"2020","unstructured":"Kia Dashtipour, Cosimo Ieracitano, Francesco Carlo Morabito, Ali Raza, and Amir Hussain. 2020. An ensemble based classification approach for Persian sentiment analysis. In Progresses in Artificial Intelligence and Neural Systems. Springer, 207\u2013215."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSICC58665.2023.10105414"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWR57742.2023.10139063"},{"key":"e_1_3_2_25_2","unstructured":"Rahim Dehkharghani and Hojjat Emami. 2020. A novel approach to sentiment analysis in Persian using discourse and external semantic information. arXiv:2007.09495 [cs.CL] (2020)."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.07.002"},{"key":"e_1_3_2_27_2","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171\u20134186."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10528-4"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946594"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551520962781"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/IALP.2009.31"},{"key":"e_1_3_2_32_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gouws Stephan","year":"2015","unstructured":"Stephan Gouws, Yoshua Bengio, and Greg Corrado. 2015. BilBOWA: Fast bilingual distributed representations without word alignments. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/isaf.1465"},{"key":"e_1_3_2_34_2","first-page":"187","volume-title":"NEAL Proceedings of the 22nd Nordic Conference on Computional Linguistics (NoDaLiDa\u201919)","author":"Hoang Mickel","year":"2019","unstructured":"Mickel Hoang, Oskar Alija Bihorac, and Jacobo Rouces. 2019. Aspect-based sentiment analysis using BERT. In NEAL Proceedings of the 22nd Nordic Conference on Computional Linguistics (NoDaLiDa\u201919). 187\u2013196."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_36_2","unstructured":"Akbar Karimi Leonardo Rossi and Andrea Prati. 2021. Improving BERT performance for aspect-based sentiment analysis. arXiv:2010.11731 [cs.CL] (2021)."},{"key":"e_1_3_2_37_2","first-page":"15349","article-title":"Systematic literature review on context-based sentiment analysis in social multimedia","author":"Kumar Akshi","year":"2019","unstructured":"Akshi Kumar and Geetanjali Garg. 2019. Systematic literature review on context-based sentiment analysis in social multimedia. Multimedia Tools and Applications 79 (2019), 15349\u201315380.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_2_38_2","first-page":"1","volume-title":"Proceedings of the World Congress on Engineering and Computer Science","volume":"1","author":"Kumar Akshi","year":"2017","unstructured":"Akshi Kumar and Arunima Jaiswal. 2017. Empirical study of Twitter and Tumblr for sentiment analysis using soft computing techniques. In Proceedings of the World Congress on Engineering and Computer Science, Vol. 1. 1\u20135."},{"issue":"4","key":"e_1_3_2_39_2","first-page":"372","article-title":"Sentiment analysis on Twitter","volume":"9","author":"Kumar Akshi","year":"2012","unstructured":"Akshi Kumar and Teeja Mary Sebastian. 2012. Sentiment analysis on Twitter. International Journal of Computer Science Issues 9, 4 (2012), 372.","journal-title":"International Journal of Computer Science Issues"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102141"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_42_2","first-page":"4663","article-title":"Applying BERT to analyze investor sentiment in stock market","author":"Li Menggang","year":"2020","unstructured":"Menggang Li, Wenrui Li, Fang Wang, Xiaojun Jia, and Guangwei Rui. 2020. Applying BERT to analyze investor sentiment in stock market. Neural Computing and Applications 33 (2020), 4663\u20134676.","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.09.057"},{"key":"e_1_3_2_44_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv:1907.11692 (2019).","journal-title":"arXiv:1907.11692"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-016-9508-4"},{"issue":"1","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.31436\/iiumej.v20i1.1036","article-title":"A combined deep learning model for Persian sentiment analysis","volume":"20","author":"Nezhad Zahra Bokaee","year":"2019","unstructured":"Zahra Bokaee Nezhad and Mohammad Ali Deihimi. 2019. A combined deep learning model for Persian sentiment analysis. IIUM Engineering Journal 20, 1 (2019), 129\u2013139.","journal-title":"IIUM Engineering Journal"},{"key":"e_1_3_2_47_2","article-title":"Leveraging ParsBERT for cross-domain polarity sentiment classification of Persian social media comments","author":"Nigjeh Mahnaz Panahandeh","year":"2023","unstructured":"Mahnaz Panahandeh Nigjeh and Shirin Ghanbari. 2023. Leveraging ParsBERT for cross-domain polarity sentiment classification of Persian social media comments. Multimedia Tools and Applications. Published online, June 24, 2023.","journal-title":"Multimedia Tools and Applications."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"issue":"8","key":"e_1_3_2_49_2","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/IranianCEE.2017.7985281","volume-title":"Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE\u201917)","author":"Roshanfekr Behnam","year":"2017","unstructured":"Behnam Roshanfekr, Shahram Khadivi, and Mohammad Rahmati. 2017. Sentiment analysis using deep learning on Persian texts. In Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE\u201917). IEEE, Los Alamitos, CA, 1503\u20131508."},{"key":"e_1_3_2_51_2","article-title":"Understanding the prediction mechanism of sentiments by XAI visualization","author":"So Chaehan","year":"2020","unstructured":"Chaehan So. 2020. Understanding the prediction mechanism of sentiments by XAI visualization. In Proceedings of the International Conference on Natural Language Processing and Information Retrieval.","journal-title":"Proceedings of the International Conference on Natural Language Processing and Information Retrieval."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50334-5_28"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/978-981-13-9282-5_34","volume-title":"Smart Intelligent Computing and Applications","author":"Somula Ramasubbareddy","year":"2020","unstructured":"Ramasubbareddy Somula, K. Dinesh Kumar, S. Aravindharamanan, and K. Govinda. 2020. Twitter sentiment analysis based on US presidential election 2016. In Smart Intelligent Computing and Applications. Springer, 363\u2013373."},{"key":"e_1_3_2_54_2","article-title":"Application of BERT to enable gene classification based on clinical evidence","volume":"2020","author":"Su Yuhan","year":"2020","unstructured":"Yuhan Su, Hongxin Xiang, Haotian Xie, Yong Yu, Shiyan Dong, Zhaogang Yang, and Na Zhao. 2020. Application of BERT to enable gene classification based on clinical evidence. BioMed Research International 2020 (2020), 5491963.","journal-title":"BioMed Research International"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.03.007"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2017.3121555"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.5555\/2390470.2390490"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012595"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488520500294"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108586"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-020-00231-3"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-021-09855-4"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.030"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1024"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626094","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:53:59Z","timestamp":1750287239000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":63,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11,30]]}},"alternative-id":["10.1145\/3626094"],"URL":"https:\/\/doi.org\/10.1145\/3626094","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]},"assertion":[{"value":"2021-12-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}